Provenance-Centered Dataset of Drug-Drug Interactions
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This document describes the LIDDI dataset, a linked dataset of drug-drug interactions with associated provenance information. The dataset combines data from multiple sources on 345 drugs and 10 adverse events. It represents drug-drug interactions as nanopublications with separate graphs for the assertion, provenance, and publication information. The dataset contains over 98,000 nanopublications and provides linkages between individual drug properties and mappings across data sources. It is intended to help prioritize drug-drug interactions found in electronic health records.
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Provenance-Centered Dataset of Drug-Drug Interactions
- 1. Provenance-Centered Dataset of Drug-Drug Interactions International Semantic Web Conference 2015 Datasets and Ontologies Track J U A N M . B A N D A 1 , T O B I A S K U H N 2 , N I G A M H . S H A H 1 , M I C H E L D U M O N T I E R 1 1 S T A N F O R D C E N T E R F O R B I O M E D I C A L I N F O R M A T I C S R E S E A R C H , S T A N F O R D , C A ; 2 D E P A R T M E N T O F H U M A N I T I E S , S O C I A L A N D P O L I T I C A L S C I E N C E S , E T H Z U R I C H , S W I T Z E R L A N D
- 2. Motivation STUDIES ANALYZING COSTS OVER TIME HAVE SHOWN THAT ADVERSE DRUG REACTIONS (ADRS) COST OVER $136 BILLION A YEAR ONE SIGNIFICANT CAUSE OF ADRS ARE DRUG-DRUG INTERACTIONS (DDIS) WHICH GREATLY AFFECT OLDER ADULTS DUE TO THE MULTIPLE DRUGS THEY ARE TAKING 1
- 3. Challenges 2
- 4. Our solution We present LIDDI – a LInked Drug-Drug Interactions nanopublication-based RDF dataset with trusty URIs LIDDI combines data from various disparate sources, always aware of their provenance due to differences in individual quality and overall confidence 3
- 5. Bonus features LIDDI provides the linking components to branch from DDIs into individual properties of each drug via Drugbank and UMLS, as well as mappings between all selected sources 4
- 6. Why nanopublications and trusty URIs? Consisting of an assertion graph with triples expressing an atomic statement, a provenance graph reporting how this assertion came about, and a publication information graph that provides meta-data for the nanopublication We want URIs that are verifiable, immutable, and permanent. Trusty URIs come with the possibility to verify with 100% confidence that a retrieved file represents the correct and original state of the resource 5
- 7. Sample DDIs Sample DDI w event Drug 1: rosuvastatin Drug 2: caspofungin Event: rhabdomyolysis Sample DDI no event Drug 1: rosuvastatin Drug 2: caspofungin 6
- 8. Dataset overview 8 data sources covering 345 drugs and 10 adverse events LIDDI 7
- 9. Data Schema: DDI We use drug-drug-interaction from SIO and PROV for provenance 8
- 10. Data Schema: Drug and mappings Contains all mappings between sources 9
- 11. How do we derive the event part of a DDI Used to annotate clinical text at Stanford [1] and for the DDI descriptions 10 [1] Iyer SV, Harpaz R, LePendu P, Bauer-Mehren A, Shah NH. Mining clinical text for signals of adverse drug-drug interactions. J Am Med Inform Assoc. 2014;21(2):353-362.
- 12. LIDDI: DDIs from HER [1] 5,983 DDI-Event (x2) 11 LIDDI [1] Iyer SV, Harpaz R, LePendu P, Bauer-Mehren A, Shah NH. Mining clinical text for signals of adverse drug-drug interactions. J Am Med Inform Assoc. 2014;21(2):353-362.
- 13. LIDDI: DDIs from public sources 21,580 DDI-Event (x2) 2,130 DDI-Event (x2) 871 DDI-Event (x2) 12 LIDDI
- 14. LIDDI: DDIs generated by prediction methods (1) [3] Tatonetti NP, Fernald GH, Altman RB. A novel signal detection algorithm for identifying hidden drug-drug interactions in adverse event reports. J Am Med Inform Assoc 2012;19:79–85. [4] Avillach P, Dufour JC, Diallo G, Salvo F, Joubert M, Thiessard F, Mougin F, Trifiro G, Fourrier-Reglat A, Pariente A, Fieschi M. Design and validation of an automated method to detect known adverse drug reactions in MEDLINE: a contribution from the EU-ADR project. J Am Med Inform Assoc. 2013;20(3):446–45 74 DDI-Event (x2) [4] 16,145 DDI-Event (x2) [3] 13 LIDDI
- 15. LIDDI: DDIs generated by prediction methods (2) 4,185 DDI no event (x2) [5] 112 DDI no event (x2) [6] 684 DDI no event [7] [5] Gottlieb A, Stein GY, Oron Y, et al. INDI: a computational framework for inferring drug interactions and their associated recommendations. Mol Syst Biol 2012;8:592. [6] Vilar S, Uriarte E, Santana L, et al. Similarity-based modeling in large-scale prediction of drug-drug interactions. Nat Protocols. 2014 09//print;9(9):2147-63. [7] Cami A, Manzi S, Arnold A, Reis BY. Pharmacointeraction Network Models Predict Unknown Drug-Drug Interactions. PLoS ONE. 2013;8(4):e61468. 12 LIDDI
- 16. Dataset Statistics 98,085 nanopublications, 392,340 total graphs, 2,051,959 triples totaling 723MB in n-quad representation 13
- 17. Usages of LIDDI Prioritization of Drug-Drug Interactions found in the EHR 14
- 18. Future Work Over 1,200 drugs are being mapped to add to this resource Add more drugs to LIDDI Add more data sources We have identified and started mapping 3 new data sources Some sources provide statistical confidence values for their DDIs Add additional information to the DDIs 15
- 19. Acknowledgements to our data providers Stanford - Assaf Gottlieb Harvard University - Aurel Cami - Ben Reis Columbia University - Santiago Vilar - George Hripcsak 16
- 20. QUESTIONS? THANK YOU 17 SPARQL endpoint: http://liddi.stanford.edu:8890/sparql Faceted Browser: http://liddi.stanford.edu:8890/fct Get: http://np.inn.ac/RA7SuQ0e661LJdKpt5EOS2DKykf1ht9LFmNaZtFSDMrXg jmbanda@stanford.edu @drjmbanda
Editor's Notes
- This is a very expensive issue that is actively researched, but people rarely combine sources or compare results
- We have more than enough statistically sound and validated methods, each one with their own particular focus on where to look for interactions. We are looking at the one based on EHR data.
- By relying on Semantic Web technologies such as RDF -- and more specifically nanopublications -- these provenance paths can be represented in an explicit and uniform manner. Consolidates multiple data sources in one cohesive linked place that allows researchers to have immediate access to multiple collections of DDI predictions from public databases and reports, biomedical literature, and methods that take different and sometimes more comprehensive approaches.
- Nano publication: Provenance graph: Data source or generation process Information graph for meta-data as creators and timestamp
- We use the PROV ontology to capture the provenance of each DDI by linking the assertion graph URI to a PROV activity. This activity is linked to the software used to generate the drug and event mappings, as well as to the citation for the method, the generation time, and the dataset where the DDIs were extracted from. In this last case we also provide a direct link to the original dataset used.
- EMR DDIs mined from at least 100 appearances, this dataset provides Adjusted odds ratios for all predictions
- FDA Adverse Event Reporting System (FAERS) We analyzed over 3.2 million reports found on the FAERS that contain drug-drug-event triples. Our restrictive threshold and drug/event space produced a total of 871 triples. Drugbank We examined a total of 2,130 new triples for this analysis. Drugs.com We used web crawling agents to mine Drugs.com for their drug-drug interaction data, producing 21,580 triples.
- TWOSIDES [2] Provides 4,651,131 potential DDI associations mined from the FDA’s FAERS using data from the first quarter of 2004 to the first quarter of 2009. We took 16,145 triples from this database. MEDLINE We adapted a method by Avilach et al. [3] in order to find DDIs using MEDLINE queries. We found a total of 74 triples.
- INferring Drug Interactions (INDI) [5] Infers interactions based on their similarity to existing known adverse events based that result from a common metabolizing enzyme (CYP). We used a total of 4,185 unique DDI tuples. Similarity-based Modeling [6] Uses drug similarity information extracted from multiple sources like: 2D and 3D molecular structure, interaction profile, target similarities, and side-effect similarities. Trained on our gold-standard we found 112 DDI tuples Predictive Pharmacointeraction Networks [7] Ppredicts unknown DDIs by exploiting the network structure of all known DDIs, alongside other intrinsic and taxonomic properties of drugs and adverse events. This method found 684 DDI predictions that match our EHR-based predicted tuples.